def hyperheuristicSolverHH(kp: Knapsack, items: List[Item], hh: Hyperheuristic, stopCritaria = 10):
# Prepare the HH variables
hh.reset()
mh = Metaheuristic()
kp_best = kp.copy()
mh_best = mh.copy()
countNone = 0
while countNone < stopCritaria:
# Choice the next heuristic
nextHeuristic = hh.getHeuristic(items)
# Apply the heuristic
nextItem = SimpleHeuristic(nextHeuristic).apply(kp, items)
if nextItem == None:
# Reject the heuristic
countNone += 1
continue
countNone = 0
# Accept the heuristic
mh.addHeuristic(nextHeuristic)
# Save the best solution reached
if kp_best.getValue() < kp.getValue():
kp_best = kp.copy()
mh_best = mh.copy()
# Return the best solution reached
return kp_best, mh_best
def SimulatedAnnealing(kp: Knapsack,
items: List[Item],
n_iterations=100,
temp=200,
stopCriteria=10):
# Simulated Annealing implementation
# Initialization of the variables
mh = Metaheuristic()
heuristics = list(heuristicComparison.keys())
countNone = 0
kp_best = kp.copy()
mh_best = Metaheuristic()
n_iterations = max(n_iterations, 2 * len(items))
for i in range(n_iterations):
if countNone == stopCriteria:
# Stop criteria met
break
# Choice randomly the next heuristic
nextHeuristic = np.random.choice(heuristics)
kp_candidate = kp.copy()
items_candidate = items.copy()
nextItem = SimpleHeuristic(nextHeuristic).apply(
kp_candidate, items_candidate)
if nextItem == None:
# Heuristic does not change the instance
countNone += 1
continue
countNone = 0
if kp_best.getValue() < kp_candidate.getValue():
# Heuristic improve the performance of the solution
kp_best = kp_candidate.copy()
mh_best = mh.copy()
mh_best.addHeuristic(nextHeuristic)
# Calculate the metropolis variable
diff = kp.getValue() - kp_candidate.getValue()
t = temp / (i + 1)
if -10 <= -diff / t and -diff / t <= 0:
metropolis = np.exp(-diff / t)
elif -diff / t <= -10:
metropolis = 0
else:
metropolis = 1
# Acceptance criteria
if diff < 0 or np.random.rand() <= metropolis:
kp = kp_candidate
items = items_candidate
mh.addHeuristic(nextHeuristic)
else:
countNone += 1
# Return the best solution reached
return kp_best, mh_best
def RandomSearch(kp: Knapsack, items: List[Item], stopCriteria=10):
# Random Search implementation
# Initialize the variables
mh = Metaheuristic()
heuristics = list(heuristicComparison.keys())
countNone = 0
while countNone < stopCriteria:
# Choice randomly the next heuristic
nextHeuristic = np.random.choice(heuristics)
kp_candidate = kp.copy()
items_candidate = items.copy()
nextItem = SimpleHeuristic(nextHeuristic).apply(
kp_candidate, items_candidate)
if nextItem == None or kp_candidate.getValue() <= kp.getValue():
# Reject the heuristic
countNone += 1
continue
countNone = 0
# Accept the heuristic
kp = kp_candidate
items = items_candidate
mh.addHeuristic(nextHeuristic)
return kp, mh